1. Field of the Invention
The disclosure relates generally to the field of scoring and prediction and to tools to improve the flexibility and efficiency in generating and evaluating aggregated attributes.
2. Description of the Related Art
There exists significant interest in information indicative of the relative financial risk or profitability of potential business transactions with individuals or other entities. For example, a lending institution is interested in the relative likelihood of a loan recipient timely and reliably making mutually agreed loan payments. An insurance company is interested in the relative likelihood of claims arising from an existing or potential client. Such predictive information is a factor in deciding whether to engage in a particular business transaction and/or the terms under which a transaction takes place.
A large variety of public records and privately developed databases can be utilized to inform such risk/benefit determinations. For example, credit reporting agencies (CRAs) collect and maintain information on a person's individual credit history. This information can include a total credit line on one or more accounts, current credit balance, credit ratios, satisfactorily paid accounts, any late payments or delinquencies, depth of credit history, total outstanding credit balance, and/or records of recent and/or historical inquiries into the person's credit. Governmental motor vehicle agencies generally maintain records of any vehicle code violations by a person as well as histories of reported accidents. Courts will generally maintain records of pending or disposed cases associated with a person, such as small claims filings, bankruptcy filings, and/or any criminal charges. Similar information also exists for large and small businesses, such as length of the business's existence, reported income, profits, outstanding accounts receivable, payment history, market share, and so forth.
The extensive amount of data available for any given person or entity makes the task of evaluating a business decision regarding the person or entity very difficult. Accordingly, such raw data is frequently processed to facilitate more convenient and rapid financial decisions. For example, a person's raw financial data can be processed to produce a “score” indicative of their relative credit worthiness. Such a score can be utilized in decisions to extend the person or entity further credit and/or as a factor in determining an interest rate to be charged. The evaluation of the relative risk/benefit of a given decision is even more complex when considering multiple persons simultaneously, such as spouses, partnerships, sole proprietorships, joint ventures, LLCs or other entities. When considering multiple persons, raw data from multiple sources about each of the individuals may need to be evaluated.
Attributes can be used to calculate various types of scores and in many instances may be used on their own to guide business decisions as well. Attributes can be aggregated to target various aspects of credit histories, bankruptcy data, and other types of non-credit-based data. For example, a simple attribute could be “consumers who have opened a new credit line in the last 12 months.” The results of the attribute would be a set of consumers who meets both the criteria of having opened a new credit line and having done so in the last 12 months. Therefore, attributes are important in facilitating the use of raw data for a variety of decisions that financial institutions and other entities may need to make.